Forensic video analysis (FVA) is an investigative, post-event forensic science, and the International Association for Identification (IAI) has formally recognized FVA as a sub-specialty within the scientific discipline of forensic imaging. Specifically, EVA is the scientific examination, comparison, and evaluation of video in legal matters. That is, FVA is the application of image science and domain expertise to interpret the content of an image or the image itself in legal matters. Disciplines of FVA with law enforcement applications include photogrammetry, photographic comparison, content analysis, and image authentication. For example, a forensic analyst may want to identify information regarding the interaction of people and objects in an easy and accurate manner and may want a detailed incident management report with artifacts supporting the same for producing in a court of law. Similarly, a legal person may want to view sufficient and untampered artifacts to articulate an incident in detail, including the people and objects involved in the incident.
The Scientific Working Group on Imaging Technology (SWGIT) sets standards for FVA and identifies the following tasks for the process of FVA: technical preparation, examination, and interpretation. During the interpretation process, specific subject matter expertise is applied to draw conclusions about video recordings or the content of those recordings. For example, drawing a conclusion about a video recording can include authenticating the video recording. Drawing a conclusion about the content of a video recording can include comparing objects or determining that an object appears different in the video than the object appears under normal lighting conditions due to the properties of the recording process, such as an infrared (IR) negative image effect on natural fibers.
Any incident management report that the interpretation process generates must comply with the SWGIT standards, meet the requirements of an analyst's agency, address a requestor's needs, and provide all relevant information in a clear and concise manner. However, there are currently no known systems or methods to perform FVA on a video repository of raw video data, as per the SWGIT standards, for example, to back track a person or object to create a story board of various incidents involving that person or object or an associated person or object. Furthermore, there are currently no known systems or methods to perform an investigation on multiple associated persons, including tracking objects associated with such persons and interactions between such persons and objects, or to create a story board of such persons and objects. This is because known systems and methods to interpret video and to generate incident management reports are manual and align with video data, not metadata.
Notwithstanding the above, known video systems generate thousands of video data streams per day, and one or more of those video data streams may contain representations of people or objects relevant to suspicious activities. However, most such video data streams exist only as data until they are overridden or flushed, not translated into metadata that can be a valuable data node for future FVA.
In view of the above, there is a continuing, ongoing need for improved systems and methods.